Center for Advanced Bioinformatics & Systems Medicine, Sookmyung Women's University, Hyochangwon-gil 52, Yongsan-gu, Seoul, 140-742, Republic of Korea.
Department of Biological Sciences, Sookmyung Women's University, Hyochangwon-gil 52, Yongsan-gu, Seoul, 140-742, Republic of Korea.
Arch Pharm Res. 2017 Aug;40(8):906-914. doi: 10.1007/s12272-017-0940-z. Epub 2017 Aug 1.
Cancer precision medicine requires clinically actionable biomarkers for patient stratification and a better prediction of clinical outcome. Although thousands of cancer-enriched mutated genes have been reported by global sequencing projects, to date, only a few oncogenic mutations have been confirmed as effective biomarkers in cancer therapies. The low frequency and varied profile (i.e., allele frequency, mutation position) of mutant genes among cancer types limit the utility of predictive biomarkers. The recent explosion of cancer transcriptome and phenotypic screening data provides another opportunity for finding transcript-level biomarkers and targets, thus overcoming the limitation of cancer mutation analyses. Technological developments enable the rapid and extensive discovery of potential target-biomarker combinations from large-scale transcriptome-level screening combined with physiologically relevant phenotypic assays. Here, we summarized recent progress as well as discussed the outlook of transcriptome-oriented data mining strategies and phenotypic assays for the identification of non-genetic biomarkers and targets in cancer drug discovery.
癌症精准医学需要具有临床可操作性的生物标志物来对患者进行分层，并更好地预测临床结果。尽管全球测序项目已经报道了数千个富含癌症的突变基因，但迄今为止，只有少数致癌突变被确认为癌症治疗中的有效生物标志物。不同癌症类型中突变基因的低频和多变特征（即等位基因频率、突变位置）限制了预测性生物标志物的应用。最近，癌症转录组和表型筛选数据的大量涌现为发现转录水平的生物标志物和靶标提供了另一个机会，从而克服了癌症突变分析的局限性。技术的发展使得从大规模转录组水平筛选结合生理相关表型测定中快速广泛地发现潜在的靶标-生物标志物组合成为可能。在这里，我们总结了最近的进展，并讨论了基于转录组的数据挖掘策略和表型测定在癌症药物发现中非遗传生物标志物和靶标的鉴定方面的前景。
